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harshmaur

GitLab MCP Server

by harshmaur

update_issue

Modify GitLab project issues by updating titles, descriptions, assignees, labels, due dates, states, and other attributes to manage project tasks.

Instructions

Update an issue in a GitLab project

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesProject ID or URL-encoded path
issue_iidYesThe internal ID of the project issue
titleNoThe title of the issue
descriptionNoThe description of the issue
assignee_idsNoArray of user IDs to assign issue to
confidentialNoSet the issue to be confidential
discussion_lockedNoFlag to lock discussions
due_dateNoDate the issue is due (YYYY-MM-DD)
labelsNoArray of label names
milestone_idNoMilestone ID to assign
state_eventNoUpdate issue state (close/reopen)
weightNoWeight of the issue (0-9)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states 'Update' implying mutation but doesn't disclose permissions needed, whether changes are reversible, rate limits, or what happens to unspecified fields. This is inadequate for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 12 parameters, no annotations, and no output schema, the description is insufficient. It lacks behavioral context (e.g., error handling, side effects), usage guidance, and output expectations, leaving significant gaps for an AI agent to operate effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 12 parameters. The description adds no parameter-specific information beyond what's in the schema, such as explaining interactions between fields or default behaviors. Baseline 3 is appropriate when schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Update') and resource ('an issue in a GitLab project'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'update_issue_note' or 'update_label', which also update GitLab resources but target different entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing issue access), exclusions (e.g., not for creating issues), or comparisons with siblings like 'update_issue_note' for note-specific updates.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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